Tracking genetic diversity of SARS-CoV-2 infections in Ghana after one year of surveillance

This study sequenced 1077 SARS-CoV-2 genomes from patient isolates (106 from arriving travellers and 971 from communities) to track the molecular evolution and spatio-temporal dynamics of the SARS-CoV-2 variants in Ghana. The data show that initial local transmission was dominated by B.1.1 lineages, but the second wave in Ghana was overwhelmingly driven by the Alpha variant, which was detected in community cases from January 2021, with Eta also contributing to reported cases. Subsequently, an unheralded variant under monitoring, B.1.1.318, dominated transmission from April to June 2021 before being displaced by Delta (B.1.1.617) and Delta Plus (AY.*) variants, which were introduced into community transmission in May 2021 and have remained dominant to date. Mutational analysis indicated that variants that took hold in Ghana harboured transmission enhancing and immune escape spike substitutions. The apparent rapid viral evolution observed demonstrate the potential for emergence of novel variants with greater mutational tness. used for the study. All data storage and analyses were performed on Zuputo®, the University of Ghana's high-performance computing cluster. We acknowledge the Community and Public engagement members; Kyerewaa A. Boateng and Simon Donkor, Andrew M. Nantogmah and the entire WACCBIP and UHAS COVID-19 Teams.


Introduction
A year after the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID- 19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a pandemic, over 190 million con rmed cases and 4 million deaths have been reported worldwide 1  COVID-19 control measures in Ghana have evolved with the global pandemic. Ghana's international airports and all land borders were closed to international travel on 22nd March 2020, followed by a partial lockdown of two major cities from March 30th to 22nd April 2020. This was coupled with enhanced testing and contact tracing to track community spread. The airport was reopened to international travel on 1st September 2020, with two-fold containment measures; (i) travellers must show proof of a negative COVID-19 test (taken at most 72 hours before arrival) and (ii) travellers must be negative for the SARS-CoV-2 antigen test upon arrival at the Kotoka International Airport (KIA) 2 . The guidelines for travellers who test positive at the airport have changed over time. Initially, travellers who tested positive upon arrival had to undergo mandatory isolation for at least 14 days (at travellers' cost) and were only allowed to go after a negative PCR/antigen test. The guidelines were later relaxed to allow self-isolation, but mandatory isolation has been reinstated due to poor compliance 2 . Currently, a positive test leads to a minimum of 3 days in isolation. After three days, travellers who test negative by RT-PCR after three days are allowed to leave isolation. Since January 2021, all positive samples from travellers have been made available for genomic sequencing.
Like other RNA viruses, most mutations in the SARS-CoV-2 genome arise naturally and spontaneously during viral replication, transmission, and adaptation cycles in the population. Whole-genome sequencing is critical in tracking viral genomic changes and may help to understand phenotypic changes. According to the WHO, several variants of interest (VOIs) have been shown to harbour genomic mutations associated with enhanced community transmission or multiple COVID-19 cases/clusters in numerous countries 6 . Other VOIs have proven to be variants of concern (VOCs) due to their increased transmissibility, virulence, and disease severity, or decreased susceptibility to public health and social measures, available diagnostics, vaccines, and therapeutics 7 . These may be demonstrated by increased receptor binding, reduced virus neutralisation by antibodies generated against previous infection or vaccination, loss or reduced diagnostic detection, or increased replication (WHO, 2021a Having established a local capacity for sequencing and analysing SARS-CoV-2 genomes in Ghana 9 , molecular surveillance has continued using samples provided by the COVID-19 testing laboratories across the country. In addition, some samples from international travellers who tested positive on arrival at the airport were also analysed to track the introduction of new variants into the country. Thus, this report provides a comprehensive analysis of the genetic diversity of SARS-CoV-2 viruses that caused infections in the communities in Ghana from June 2020 to September 2021.
In 2021, there was a marked shift in the circulating variants and occurrence of regional speci c outbreaks, with Eta dominating in Northern and middle belt regions, while B.1.1.318 dominated the major cities. The highest frequencies of Eta variants were observed in the Northern (23.6 %, 13/55), Bono (30.8%, 4/13) and Eastern (13%, 3/23) regions. The city of Tamale in the Northern region is the gateway, and central trading hub with Ghana's northern neighbours, whilst the Bono region harbours major interaction routes with Ivory Coast in the western corridor of Ghana. Meanwhile, a third of all the variants detected in 2021 were B.1.1.318 (30%, 176/802), and Greater Accra, where the capital city and the major international airport are located, had 80% (140/176) of all the genomes. (Fig. 1d). These data suggest that Eta and B.1.1.318 variants, which dominated transmission in these areas in April-May 2021, could have been introduced through these major land borders. Interestingly, the B.1. 1, B.1.359, B.1.1., and B.1.623 that dominated Ghana in 2020 became supplanted by the modal variants responsible for most transmissions in all the regions. It is worth noting that in regions where more than 50 samples were sequenced in 2021, there was penetration or transmission of all the VOCs, including the Central region, Greater Accra, and Volta Region (Fig. 1d).

Importation of SARS-CoV-2 variants into Ghana by travellers
One hundred and six of the sequenced samples (9.8%, 106/1077) were obtained from quarantined travellers identi ed as COVID-19 positive at the KIA. Of this number, Alpha accounted for 44.3% (n=47) of the genomes while the other VOCs accounted for lower proportions; Beta (6.6 %, n=7), Delta (6.6 %, n=7), and Delta Plus (0.9 %, n=1) (Fig. 2a). The VOIs such as Eta (4.7 %, n=5), Kappa and the local variantunder-monitoring, B.1.1.318, 3.8 % (n=4) were detected at low proportions. Importantly, the VOC Alpha was identi ed in travellers entering Ghana from all over the World, including other African countries, in January and March 2021 ( Fig. 2b and c). Furthermore, VOCs were detected in travellers from several of Ghana's neighbouring countries, demonstrating that these variants were already in those countries even though not reported or detected (Fig. 2c). In most cases, VOCs and VOIs were identi ed amongst quarantined travellers before their detection within local samples. Travellers from Nigeria, Dubai, and the UK accounted for most detections of Alpha, Beta, Delta, Eta, and Delta-plus variants. Interestingly, the Beta and Kappa variants did not become dominant in Ghana; instead, B.1.1.318, which is likely to have originated from Nigeria, and detected in a traveller from Gabon, became dominant in Ghana.

Temporal trends of SARS-CoV-2 variant detection and frequency
Ghana was one of the last African countries to detect COVID-19 cases in March 2020, and the waves of COVID-19 in Ghana have lagged slightly behind other African countries and signi cantly behind the rest of the World (Fig. 3a). Previous work from our group described the viral genome dynamics between March and June 2020, when Ghana was largely closed to international travel (Ngoi et al., 2021). WHO (2021) reported that different variants rose to dominance at different times and during different infection waves across the country ( Fig. 3a and 3b). Variants that cluster closely to B.1.1 were rst detected in June 2020 (44.4%, 4/9) and peaked in July 2020 (58.

Genetic diversity and evolutionary relationships of the SARS-CoV-2 variants
Amongst the many individual lineages represented in the data presented here, Delta, Delta Plus, Alpha, B.1.1.318, B.1.1.359, B.1.1, and Eta were the most evolved, with the highest genetic diversity (Fig. 4a). These variants exhibited a local variation in the number of mutations from sample to sample, with Delta, Alpha and B.1.1.318 presenting a mean ~30 (spread/range of 20-45) mutations in the majority of the genomes (Fig. 4a). The VOC delta plus, a subline of the Delta, had the highest mean (~35) and presented a range of mutations from 25 to 45 mutations across all the samples (Fig. 4a). It is worth noting that this level of genetic diversity in the 200 Delta plus samples mainly was attributed to the AY.39 (174/200) and AY.37 (15/200) lineages. Most of the other lineages with a small range of mutations were reported in 2020 and occurred spontaneously in very few samples hence the relatively low genetic diversity (Fig. 4a).
The high level of genetic diversity in most VOCs, including the B.1.1318, is probably indicative of Ghana's local evolution and consequential adaptation compared to the other variants that did not gain prominence in the Ghanaian population (Fig. 4a).
A snapshot of the evolutionary relationship of these VOCs in Ghana shows an exciting relationship of variants through space and time throughout the epidemic (Fig. 4b). Using a phylogenetic tree, we outline the transmission events, how the VOCs were introduced, and how they gained prominence coinciding with the COVID-19 waves in Ghana. The outbreak of the COVID-19 pandemic started in mid-November 2019, but then the tree shows that the earliest lineages in Ghana are dated June 2020, although most VOCs were introduced in 2021 (Fig. 4b). The phylogenetic analysis of the genomes from Ghana shows similarities to VOCs around the World, with all the VOCs having the same common ancestor (Wuhan). Still, as they diverge, they share uniquely more recent ancestors; for example, we show that the recently classi ed sub-lineages of Delta share several recent ancestors in the same clade (Fig. 4b). The root-to-tip divergence of the VOCs as a function of sampling time show a molecular clock of the various VOCs, and with strong evidence, the variants are evolving in a clocklike manner (R 2 =0.68) (Fig. 4c). The variants in Ghana are gaining 26.54 mutations per year, and of particular interest is the B.1.1.318 that did not gain prominence worldwide, but its molecular clock is similar to most of the VOCs in Ghana (Fig. 4c). Mutational tness of the B.1.1.318 lineage showed that ten samples had spike mutations that were likely to confer viral tness (mutational tness >1) (Fig. 4c).

Mutational analysis of the amino acid substitutions
The most abundant mutation in all the samples was the Spike D614G (98%, 957/971), followed by ORF1b: P314L (92%, 900/971) (Fig. 5a). For many of the genes, one or more mutations were occurred in more than 100 samples, although spike protein dominated the pro le (Fig. 5a). Interestingly, some variants with different evolutionary lineages had similar amino acid substitutions, mainly spike glycoprotein. The Eta variant had the highest (three) individual mutations (Q52R, Q677H and F888L) in the spike protein compared to other VOCs, contributing to its adaptability in Africa. Compared to other VOCs, the mutations unique to the Alpha variant were S13I, R567K, A570D, and T716I. The only mutation unique to the B.1.1.318 on the spike protein was the D1127G compared to other VOCs and VOIs. (Fig. 5b). Within these samples, the Delta and Delta plus shared fourteen mutations (T19R, G142D, R158G, A222V, L452R, T478K, E484Q, D614G, S680F, P681R, D950N, K1191N, G1219V and C1253F), but the Delta plus had P26L as a unique mutation (Fig. 5b). The unique mutations were fewer than shared mutations, thus explaining the increased abundance of some mutations among the VOCs. Those with the highest frequency in the spike protein among Delta plus, Delta, B.1, B.1.1, B.1.1.318, Alpha, Beta and Eta were tness substitutions D614G and P681R/H (Fig. 5b). Alpha and B.1.1.318 had the P681H substitution while P681R was present in Delta, Eta and Delta-plus variants. Immune escape mutation E484K was present in B.1.1.318, Beta, and Eta, while Delta-plus variants frequently presented with E484Q mutation. and Upper East, further from Accra, tended to have different variants during the second wave. In the third wave, these regions still lag behind the rest of the country and do not seem to be undergoing a third wave yet 2 . These regions experience much lower international travellers from global COVID-19 hotspots than Greater Accra and Ashanti. Furthermore, they have a more sparse population with less congested cities than Greater Accra and Ashanti regions. Studies have shown that higher population density increases contact rates necessary for SARS-CoV-2 disease transmission 10 .

Discussion
Before the airport reopened to international travellers in September 2020, the B.1.1 variant was dominant in Ghana and remained the most dominant circulating lineage throughout 2020. In January 2021, the Alpha variant supplanted B.1.1 and became the leading cause of all reported cases nationally. Our analysis showed that major variants such as Alpha, Beta, Delta, Delta Plus, Eta, and Kappa were detected in samples from arriving travellers before being observed in community cases. This was indicative that VOCs and VOIs were likely imported into Ghana through travellers from other countries, including African countries which had not reported such variants. These results also suggest that the delayed entry of VOCs into Ghana was at least partly due to the mandatory antigen testing on arrival at Kotoka International Airport (introduced when the borders were reopened in September 2020) and subsequent imposition of a policy for compulsory isolation of all antigen-positive passengers (in January 2021). The results also suggest that some individuals escaped detection and seeded local outbreaks. Alpha was the most frequent variant among travellers, followed by the Delta, Beta, Eta, and B.  21,22 . Despite very low vaccinations rates and the dominance of Delta and its sub-lineages, severe infections and Case Fatality Rates remain low to date, a further indication of the apparent resilience of the Ghanaian population to COVID-19 highlighted in our previous study 5 . Nevertheless, as a limitation to the study, the dominance of the VOC in our study may result from the purposive sampling and potential underrepresentation of some variants in other regions.
Through continuous genomic surveillance, we have characterised the diversity and evolution of COVID-19 variants in Ghana. We observed high variation in the number of mutations between samples, suggesting rapid evolution and multiple independent emergences of the Ghanaian variants against the backdrop of a multi-ethnic society. Besides, high mutation frequency within a population leads to higher chances of diverse variants. Although our study has the limitation of relying on purposive sampling, the large sample size and the depth of genetic analysis performed give high con dence that these data are robust and provide a credible overview of the evolution of the pandemic in Ghana. Apparent resilience to disease severity notwithstanding, this study indicates that Ghana is not impervious to the entry of novel variants nor immune to their increased pathogenesis. Enhancing and speeding up vaccinations should be a priority, as well as the pursuit of therapeutic options. Ongoing virus and virus-host interaction experiments, combined with enhanced studies on the severe/critically ill patients, should also give more insights into the pathogenesis of SARS-CoV-2 in Ghana.

Study Participants
The study was approved by the Ethics according to the ARTIC bioinformatic protocols 23 . Sequencing QC was performed using pycoQC, and predemultiplexed reads were aggregated and length ltered using ARTIC guppyplex for a minimum of 400 reads and a maximum of 700 reads to remove chimeric reads. Read QC was performed using NanoPlot before reading alignment, variant calling and consensus generations using ARTIC Genomes that pass quality control were deposited on GISAID. Phylogenetic assignment of the consensus sequence to the globally named outbreak lineages was performed using Pangolin (